Overview

Dataset statistics

Number of variables28
Number of observations8674
Missing cells8659
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory224.0 B

Variable types

Categorical11
DateTime1
TimeSeries14
Numeric2

Alerts

State Code has constant value "4"Constant
County Code has constant value "13"Constant
Site Num has constant value "3003"Constant
Address has constant value "2857 N MILLER RD-S SCOTTSDALE STN"Constant
State has constant value "Arizona"Constant
County has constant value "Maricopa"Constant
City has constant value "Scottsdale"Constant
NO2 Units has constant value "Parts per billion"Constant
O3 Units has constant value "Parts per million"Constant
SO2 Units has constant value "Parts per billion"Constant
CO Units has constant value "Parts per million"Constant
NO2 Mean is highly overall correlated with NO2 1st Max Value and 7 other fieldsHigh correlation
NO2 1st Max Value is highly overall correlated with NO2 Mean and 4 other fieldsHigh correlation
NO2 AQI is highly overall correlated with NO2 Mean and 4 other fieldsHigh correlation
O3 Mean is highly overall correlated with NO2 Mean and 5 other fieldsHigh correlation
O3 1st Max Value is highly overall correlated with O3 Mean and 1 other fieldsHigh correlation
O3 AQI is highly overall correlated with O3 Mean and 1 other fieldsHigh correlation
SO2 Mean is highly overall correlated with SO2 1st Max Value and 1 other fieldsHigh correlation
SO2 1st Max Value is highly overall correlated with NO2 Mean and 5 other fieldsHigh correlation
SO2 AQI is highly overall correlated with NO2 Mean and 5 other fieldsHigh correlation
CO Mean is highly overall correlated with NO2 Mean and 7 other fieldsHigh correlation
CO 1st Max Value is highly overall correlated with NO2 Mean and 7 other fieldsHigh correlation
CO AQI is highly overall correlated with NO2 Mean and 7 other fieldsHigh correlation
SO2 AQI has 4336 (50.0%) missing valuesMissing
CO AQI has 4323 (49.8%) missing valuesMissing
NO2 Mean is non stationaryNon stationary
NO2 1st Max Value is non stationaryNon stationary
NO2 AQI is non stationaryNon stationary
O3 Mean is non stationaryNon stationary
O3 1st Max Value is non stationaryNon stationary
O3 AQI is non stationaryNon stationary
SO2 Mean is non stationaryNon stationary
SO2 1st Max Value is non stationaryNon stationary
SO2 1st Max Hour is non stationaryNon stationary
SO2 AQI is non stationaryNon stationary
CO Mean is non stationaryNon stationary
CO AQI is non stationaryNon stationary
NO2 Mean is seasonalSeasonal
NO2 1st Max Value is seasonalSeasonal
NO2 AQI is seasonalSeasonal
O3 Mean is seasonalSeasonal
O3 1st Max Value is seasonalSeasonal
O3 AQI is seasonalSeasonal
SO2 Mean is seasonalSeasonal
SO2 1st Max Value is seasonalSeasonal
SO2 1st Max Hour is seasonalSeasonal
SO2 AQI is seasonalSeasonal
CO Mean is seasonalSeasonal
CO AQI is seasonalSeasonal
NO2 1st Max Hour has 660 (7.6%) zerosZeros
O3 1st Max Hour has 148 (1.7%) zerosZeros
SO2 Mean has 218 (2.5%) zerosZeros
SO2 1st Max Value has 218 (2.5%) zerosZeros
SO2 1st Max Hour has 941 (10.8%) zerosZeros
SO2 AQI has 108 (1.2%) zerosZeros
CO 1st Max Hour has 2160 (24.9%) zerosZeros

Reproduction

Analysis started2023-01-25 13:26:57.274932
Analysis finished2023-01-25 13:27:39.622154
Duration42.35 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

State Code
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
4
8674 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8674
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 8674
100.0%

Length

2023-01-25T13:27:39.670589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T13:27:39.785695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
4 8674
100.0%

Most occurring characters

ValueCountFrequency (%)
4 8674
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8674
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8674
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 8674
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8674
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 8674
100.0%

County Code
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
13
8674 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters17348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
13 8674
100.0%

Length

2023-01-25T13:27:39.875811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T13:27:39.991048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
13 8674
100.0%

Most occurring characters

ValueCountFrequency (%)
1 8674
50.0%
3 8674
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8674
50.0%
3 8674
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8674
50.0%
3 8674
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8674
50.0%
3 8674
50.0%

Site Num
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
3003
8674 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters34696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3003
2nd row3003
3rd row3003
4th row3003
5th row3003

Common Values

ValueCountFrequency (%)
3003 8674
100.0%

Length

2023-01-25T13:27:40.082077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T13:27:40.199455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3003 8674
100.0%

Most occurring characters

ValueCountFrequency (%)
3 17348
50.0%
0 17348
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34696
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 17348
50.0%
0 17348
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 17348
50.0%
0 17348
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 17348
50.0%
0 17348
50.0%

Address
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
2857 N MILLER RD-S SCOTTSDALE STN
8674 

Length

Max length33
Median length33
Mean length33
Min length33

Characters and Unicode

Total characters286242
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2857 N MILLER RD-S SCOTTSDALE STN
2nd row2857 N MILLER RD-S SCOTTSDALE STN
3rd row2857 N MILLER RD-S SCOTTSDALE STN
4th row2857 N MILLER RD-S SCOTTSDALE STN
5th row2857 N MILLER RD-S SCOTTSDALE STN

Common Values

ValueCountFrequency (%)
2857 N MILLER RD-S SCOTTSDALE STN 8674
100.0%

Length

2023-01-25T13:27:40.291328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T13:27:40.407699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2857 8674
16.7%
n 8674
16.7%
miller 8674
16.7%
rd-s 8674
16.7%
scottsdale 8674
16.7%
stn 8674
16.7%

Most occurring characters

ValueCountFrequency (%)
43370
15.2%
S 34696
12.1%
T 26022
 
9.1%
L 26022
 
9.1%
E 17348
 
6.1%
D 17348
 
6.1%
N 17348
 
6.1%
R 17348
 
6.1%
O 8674
 
3.0%
C 8674
 
3.0%
Other values (8) 69392
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 199502
69.7%
Space Separator 43370
 
15.2%
Decimal Number 34696
 
12.1%
Dash Punctuation 8674
 
3.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 34696
17.4%
T 26022
13.0%
L 26022
13.0%
E 17348
8.7%
D 17348
8.7%
N 17348
8.7%
R 17348
8.7%
O 8674
 
4.3%
C 8674
 
4.3%
I 8674
 
4.3%
Other values (2) 17348
8.7%
Decimal Number
ValueCountFrequency (%)
2 8674
25.0%
8 8674
25.0%
7 8674
25.0%
5 8674
25.0%
Space Separator
ValueCountFrequency (%)
43370
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 199502
69.7%
Common 86740
30.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 34696
17.4%
T 26022
13.0%
L 26022
13.0%
E 17348
8.7%
D 17348
8.7%
N 17348
8.7%
R 17348
8.7%
O 8674
 
4.3%
C 8674
 
4.3%
I 8674
 
4.3%
Other values (2) 17348
8.7%
Common
ValueCountFrequency (%)
43370
50.0%
- 8674
 
10.0%
2 8674
 
10.0%
8 8674
 
10.0%
7 8674
 
10.0%
5 8674
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 286242
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43370
15.2%
S 34696
12.1%
T 26022
 
9.1%
L 26022
 
9.1%
E 17348
 
6.1%
D 17348
 
6.1%
N 17348
 
6.1%
R 17348
 
6.1%
O 8674
 
3.0%
C 8674
 
3.0%
Other values (8) 69392
24.2%

State
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Arizona
8674 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters60718
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona 8674
100.0%

Length

2023-01-25T13:27:40.502866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T13:27:40.617862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
arizona 8674
100.0%

Most occurring characters

ValueCountFrequency (%)
A 8674
14.3%
r 8674
14.3%
i 8674
14.3%
z 8674
14.3%
o 8674
14.3%
n 8674
14.3%
a 8674
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 52044
85.7%
Uppercase Letter 8674
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 8674
16.7%
i 8674
16.7%
z 8674
16.7%
o 8674
16.7%
n 8674
16.7%
a 8674
16.7%
Uppercase Letter
ValueCountFrequency (%)
A 8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 60718
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 8674
14.3%
r 8674
14.3%
i 8674
14.3%
z 8674
14.3%
o 8674
14.3%
n 8674
14.3%
a 8674
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 8674
14.3%
r 8674
14.3%
i 8674
14.3%
z 8674
14.3%
o 8674
14.3%
n 8674
14.3%
a 8674
14.3%

County
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Maricopa
8674 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters69392
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaricopa
2nd rowMaricopa
3rd rowMaricopa
4th rowMaricopa
5th rowMaricopa

Common Values

ValueCountFrequency (%)
Maricopa 8674
100.0%

Length

2023-01-25T13:27:40.710611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T13:27:40.824604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
maricopa 8674
100.0%

Most occurring characters

ValueCountFrequency (%)
a 17348
25.0%
M 8674
12.5%
r 8674
12.5%
i 8674
12.5%
c 8674
12.5%
o 8674
12.5%
p 8674
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 60718
87.5%
Uppercase Letter 8674
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 17348
28.6%
r 8674
14.3%
i 8674
14.3%
c 8674
14.3%
o 8674
14.3%
p 8674
14.3%
Uppercase Letter
ValueCountFrequency (%)
M 8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 69392
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 17348
25.0%
M 8674
12.5%
r 8674
12.5%
i 8674
12.5%
c 8674
12.5%
o 8674
12.5%
p 8674
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 17348
25.0%
M 8674
12.5%
r 8674
12.5%
i 8674
12.5%
c 8674
12.5%
o 8674
12.5%
p 8674
12.5%

City
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Scottsdale
8674 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters86740
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowScottsdale
2nd rowScottsdale
3rd rowScottsdale
4th rowScottsdale
5th rowScottsdale

Common Values

ValueCountFrequency (%)
Scottsdale 8674
100.0%

Length

2023-01-25T13:27:40.916905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T13:27:41.031353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
scottsdale 8674
100.0%

Most occurring characters

ValueCountFrequency (%)
t 17348
20.0%
S 8674
10.0%
c 8674
10.0%
o 8674
10.0%
s 8674
10.0%
d 8674
10.0%
a 8674
10.0%
l 8674
10.0%
e 8674
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 78066
90.0%
Uppercase Letter 8674
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 17348
22.2%
c 8674
11.1%
o 8674
11.1%
s 8674
11.1%
d 8674
11.1%
a 8674
11.1%
l 8674
11.1%
e 8674
11.1%
Uppercase Letter
ValueCountFrequency (%)
S 8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 86740
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 17348
20.0%
S 8674
10.0%
c 8674
10.0%
o 8674
10.0%
s 8674
10.0%
d 8674
10.0%
a 8674
10.0%
l 8674
10.0%
e 8674
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 17348
20.0%
S 8674
10.0%
c 8674
10.0%
o 8674
10.0%
s 8674
10.0%
d 8674
10.0%
a 8674
10.0%
l 8674
10.0%
e 8674
10.0%
Distinct2176
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Minimum2000-01-01 00:00:00
Maximum2010-12-31 00:00:00
2023-01-25T13:27:41.149008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:41.302794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Parts per billion
8674 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters147458
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion 8674
100.0%

Length

2023-01-25T13:27:41.436026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T13:27:41.551068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
parts 8674
33.3%
per 8674
33.3%
billion 8674
33.3%

Most occurring characters

ValueCountFrequency (%)
r 17348
11.8%
17348
11.8%
i 17348
11.8%
l 17348
11.8%
P 8674
 
5.9%
a 8674
 
5.9%
t 8674
 
5.9%
s 8674
 
5.9%
p 8674
 
5.9%
e 8674
 
5.9%
Other values (3) 26022
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 121436
82.4%
Space Separator 17348
 
11.8%
Uppercase Letter 8674
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 17348
14.3%
i 17348
14.3%
l 17348
14.3%
a 8674
7.1%
t 8674
7.1%
s 8674
7.1%
p 8674
7.1%
e 8674
7.1%
b 8674
7.1%
o 8674
7.1%
Space Separator
ValueCountFrequency (%)
17348
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 130110
88.2%
Common 17348
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 17348
13.3%
i 17348
13.3%
l 17348
13.3%
P 8674
6.7%
a 8674
6.7%
t 8674
6.7%
s 8674
6.7%
p 8674
6.7%
e 8674
6.7%
b 8674
6.7%
Other values (2) 17348
13.3%
Common
ValueCountFrequency (%)
17348
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 17348
11.8%
17348
11.8%
i 17348
11.8%
l 17348
11.8%
P 8674
 
5.9%
a 8674
 
5.9%
t 8674
 
5.9%
s 8674
 
5.9%
p 8674
 
5.9%
e 8674
 
5.9%
Other values (3) 26022
17.6%

NO2 Mean
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct1152
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.616994
Minimum0
Maximum139.54167
Zeros4
Zeros (%)< 0.1%
Memory size67.9 KiB
2023-01-25T13:27:41.653065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.833333
Q116.458333
median21.666667
Q326.958333
95-th percentile35.178334
Maximum139.54167
Range139.54167
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation11.641916
Coefficient of variation (CV)0.51474198
Kurtosis26.089449
Mean22.616994
Median Absolute Deviation (MAD)5.25
Skewness3.717748
Sum196179.81
Variance135.53422
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.81690497 × 10-11
2023-01-25T13:27:41.804267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.583333 44
 
0.5%
20.333333 36
 
0.4%
18.166667 32
 
0.4%
19.166667 32
 
0.4%
17 32
 
0.4%
20.708333 32
 
0.4%
19.041667 32
 
0.4%
19.208333 32
 
0.4%
24.166667 28
 
0.3%
20.958333 28
 
0.3%
Other values (1142) 8346
96.2%
ValueCountFrequency (%)
0 4
< 0.1%
0.5 4
< 0.1%
1.555556 4
< 0.1%
2 4
< 0.1%
2.761905 2
< 0.1%
3.222222 4
< 0.1%
3.583333 4
< 0.1%
3.608696 4
< 0.1%
3.611111 4
< 0.1%
3.625 4
< 0.1%
ValueCountFrequency (%)
139.541667 4
< 0.1%
135.333333 4
< 0.1%
135.1875 4
< 0.1%
123.333333 4
< 0.1%
113.083333 4
< 0.1%
110.136364 4
< 0.1%
107.545455 4
< 0.1%
105.5 4
< 0.1%
98.75 4
< 0.1%
97.458333 4
< 0.1%
2023-01-25T13:27:41.981192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Value
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct121
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.715933
Minimum0
Maximum267
Zeros4
Zeros (%)< 0.1%
Memory size67.9 KiB
2023-01-25T13:27:42.231335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23
Q136
median44
Q351
95-th percentile63
Maximum267
Range267
Interquartile range (IQR)15

Descriptive statistics

Standard deviation24.22286
Coefficient of variation (CV)0.52985596
Kurtosis30.916789
Mean45.715933
Median Absolute Deviation (MAD)7
Skewness4.8095303
Sum396540
Variance586.74692
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.383299232 × 10-10
2023-01-25T13:27:42.373896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 372
 
4.3%
47 342
 
3.9%
50 334
 
3.9%
41 330
 
3.8%
45 328
 
3.8%
43 306
 
3.5%
44 300
 
3.5%
48 298
 
3.4%
42 282
 
3.3%
39 268
 
3.1%
Other values (111) 5514
63.6%
ValueCountFrequency (%)
0 4
 
< 0.1%
2 4
 
< 0.1%
7 16
0.2%
8 8
 
0.1%
9 16
0.2%
10 18
0.2%
12 24
0.3%
13 20
0.2%
14 8
 
0.1%
15 16
0.2%
ValueCountFrequency (%)
267 4
< 0.1%
256 4
< 0.1%
244 4
< 0.1%
241 4
< 0.1%
233 4
< 0.1%
231 4
< 0.1%
229 4
< 0.1%
225 4
< 0.1%
224 4
< 0.1%
223 4
< 0.1%
2023-01-25T13:27:42.540581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Hour
Numeric time series

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.267466
Minimum0
Maximum23
Zeros660
Zeros (%)7.6%
Memory size67.9 KiB
2023-01-25T13:27:42.788639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median19
Q320
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.4185778
Coefficient of variation (CV)0.39456531
Kurtosis1.1789465
Mean16.267466
Median Absolute Deviation (MAD)1
Skewness-1.6050833
Sum141104
Variance41.198141
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.122748134 × 10-25
2023-01-25T13:27:42.915222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
19 1942
22.4%
18 1880
21.7%
20 1452
16.7%
21 760
 
8.8%
0 660
 
7.6%
17 340
 
3.9%
7 296
 
3.4%
22 294
 
3.4%
23 218
 
2.5%
8 204
 
2.4%
Other values (14) 628
 
7.2%
ValueCountFrequency (%)
0 660
7.6%
1 84
 
1.0%
2 56
 
0.6%
3 12
 
0.1%
4 16
 
0.2%
5 36
 
0.4%
6 192
 
2.2%
7 296
3.4%
8 204
 
2.4%
9 64
 
0.7%
ValueCountFrequency (%)
23 218
 
2.5%
22 294
 
3.4%
21 760
 
8.8%
20 1452
16.7%
19 1942
22.4%
18 1880
21.7%
17 340
 
3.9%
16 36
 
0.4%
15 20
 
0.2%
14 16
 
0.2%
2023-01-25T13:27:43.066899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

NO2 AQI
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct98
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.991469
Minimum0
Maximum132
Zeros4
Zeros (%)< 0.1%
Memory size67.9 KiB
2023-01-25T13:27:43.469213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q134
median42
Q348
95-th percentile61
Maximum132
Range132
Interquartile range (IQR)14

Descriptive statistics

Standard deviation15.822729
Coefficient of variation (CV)0.37680817
Kurtosis9.595034
Mean41.991469
Median Absolute Deviation (MAD)7
Skewness2.229427
Sum364234
Variance250.35874
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.365294294 × 10-9
2023-01-25T13:27:43.619912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 628
 
7.2%
43 372
 
4.3%
44 342
 
3.9%
47 334
 
3.9%
39 330
 
3.8%
41 306
 
3.5%
45 298
 
3.4%
40 282
 
3.3%
37 268
 
3.1%
46 268
 
3.1%
Other values (88) 5246
60.5%
ValueCountFrequency (%)
0 4
 
< 0.1%
2 4
 
< 0.1%
7 16
 
0.2%
8 24
0.3%
9 18
0.2%
11 24
0.3%
12 20
0.2%
13 8
 
0.1%
14 16
 
0.2%
15 42
0.5%
ValueCountFrequency (%)
132 4
 
< 0.1%
130 4
 
< 0.1%
128 4
 
< 0.1%
127 4
 
< 0.1%
126 8
0.1%
125 4
 
< 0.1%
124 16
0.2%
123 12
0.1%
121 8
0.1%
120 4
 
< 0.1%
2023-01-25T13:27:43.793451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

O3 Units
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Parts per million
8674 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters147458
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million 8674
100.0%

Length

2023-01-25T13:27:44.037344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T13:27:44.153094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
parts 8674
33.3%
per 8674
33.3%
million 8674
33.3%

Most occurring characters

ValueCountFrequency (%)
r 17348
11.8%
17348
11.8%
i 17348
11.8%
l 17348
11.8%
P 8674
 
5.9%
a 8674
 
5.9%
t 8674
 
5.9%
s 8674
 
5.9%
p 8674
 
5.9%
e 8674
 
5.9%
Other values (3) 26022
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 121436
82.4%
Space Separator 17348
 
11.8%
Uppercase Letter 8674
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 17348
14.3%
i 17348
14.3%
l 17348
14.3%
a 8674
7.1%
t 8674
7.1%
s 8674
7.1%
p 8674
7.1%
e 8674
7.1%
m 8674
7.1%
o 8674
7.1%
Space Separator
ValueCountFrequency (%)
17348
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 130110
88.2%
Common 17348
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 17348
13.3%
i 17348
13.3%
l 17348
13.3%
P 8674
6.7%
a 8674
6.7%
t 8674
6.7%
s 8674
6.7%
p 8674
6.7%
e 8674
6.7%
m 8674
6.7%
Other values (2) 17348
13.3%
Common
ValueCountFrequency (%)
17348
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 17348
11.8%
17348
11.8%
i 17348
11.8%
l 17348
11.8%
P 8674
 
5.9%
a 8674
 
5.9%
t 8674
 
5.9%
s 8674
 
5.9%
p 8674
 
5.9%
e 8674
 
5.9%
Other values (3) 26022
17.6%

O3 Mean
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct854
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.019104288
Minimum0.001
Maximum0.045944
Zeros0
Zeros (%)0.0%
Memory size67.9 KiB
2023-01-25T13:27:44.251736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.006737
Q10.012333
median0.018211
Q30.025042
95-th percentile0.034375
Maximum0.045944
Range0.044944
Interquartile range (IQR)0.012709

Descriptive statistics

Standard deviation0.008581389
Coefficient of variation (CV)0.44918653
Kurtosis-0.36213725
Mean0.019104288
Median Absolute Deviation (MAD)0.006206
Skewness0.43234702
Sum165.7106
Variance7.3640237 × 10-5
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.838970721 × 10-6
2023-01-25T13:27:44.399658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.011167 52
 
0.6%
0.01425 40
 
0.5%
0.009292 32
 
0.4%
0.016292 32
 
0.4%
0.017375 32
 
0.4%
0.008667 32
 
0.4%
0.020125 32
 
0.4%
0.020375 32
 
0.4%
0.020292 28
 
0.3%
0.026917 28
 
0.3%
Other values (844) 8334
96.1%
ValueCountFrequency (%)
0.001 4
< 0.1%
0.001167 4
< 0.1%
0.001917 4
< 0.1%
0.001958 4
< 0.1%
0.002125 4
< 0.1%
0.002389 4
< 0.1%
0.0025 4
< 0.1%
0.002625 4
< 0.1%
0.00275 4
< 0.1%
0.002875 4
< 0.1%
ValueCountFrequency (%)
0.045944 4
< 0.1%
0.045875 4
< 0.1%
0.045833 4
< 0.1%
0.045167 4
< 0.1%
0.045042 2
< 0.1%
0.044833 4
< 0.1%
0.044792 4
< 0.1%
0.044375 4
< 0.1%
0.044125 4
< 0.1%
0.043208 4
< 0.1%
2023-01-25T13:27:44.570774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Value
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct76
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03657021
Minimum0.001
Maximum0.078
Zeros0
Zeros (%)0.0%
Memory size67.9 KiB
2023-01-25T13:27:44.825065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.016
Q10.028
median0.036
Q30.045
95-th percentile0.057
Maximum0.078
Range0.077
Interquartile range (IQR)0.017

Descriptive statistics

Standard deviation0.012372061
Coefficient of variation (CV)0.3383098
Kurtosis-0.017078732
Mean0.03657021
Median Absolute Deviation (MAD)0.008
Skewness0.1098211
Sum317.21
Variance0.00015306788
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.157927082 × 10-5
2023-01-25T13:27:44.985719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.039 350
 
4.0%
0.035 320
 
3.7%
0.031 304
 
3.5%
0.037 292
 
3.4%
0.033 284
 
3.3%
0.032 280
 
3.2%
0.034 274
 
3.2%
0.038 264
 
3.0%
0.03 264
 
3.0%
0.042 258
 
3.0%
Other values (66) 5784
66.7%
ValueCountFrequency (%)
0.001 4
 
< 0.1%
0.002 4
 
< 0.1%
0.003 12
 
0.1%
0.004 8
 
0.1%
0.005 4
 
< 0.1%
0.006 12
 
0.1%
0.007 28
0.3%
0.008 12
 
0.1%
0.009 36
0.4%
0.01 4
 
< 0.1%
ValueCountFrequency (%)
0.078 8
 
0.1%
0.077 4
 
< 0.1%
0.075 4
 
< 0.1%
0.073 4
 
< 0.1%
0.072 4
 
< 0.1%
0.071 16
0.2%
0.07 6
 
0.1%
0.069 8
 
0.1%
0.068 20
0.2%
0.067 8
 
0.1%
2023-01-25T13:27:45.170851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Hour
Numeric time series

Distinct23
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8872493
Minimum0
Maximum23
Zeros148
Zeros (%)1.7%
Memory size67.9 KiB
2023-01-25T13:27:45.421115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q19
median10
Q310
95-th percentile12
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.6696282
Coefficient of variation (CV)0.27000717
Kurtosis12.370476
Mean9.8872493
Median Absolute Deviation (MAD)1
Skewness1.685978
Sum85762
Variance7.1269145
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.316671286 × 10-23
2023-01-25T13:27:45.543567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10 3580
41.3%
9 2818
32.5%
11 974
 
11.2%
8 472
 
5.4%
12 174
 
2.0%
0 148
 
1.7%
23 80
 
0.9%
22 60
 
0.7%
7 56
 
0.6%
13 54
 
0.6%
Other values (13) 258
 
3.0%
ValueCountFrequency (%)
0 148
 
1.7%
1 12
 
0.1%
2 8
 
0.1%
4 4
 
< 0.1%
5 8
 
0.1%
6 44
 
0.5%
7 56
 
0.6%
8 472
 
5.4%
9 2818
32.5%
10 3580
41.3%
ValueCountFrequency (%)
23 80
0.9%
22 60
0.7%
21 52
0.6%
20 40
0.5%
19 36
0.4%
18 10
 
0.1%
17 12
 
0.1%
16 4
 
< 0.1%
15 4
 
< 0.1%
14 24
 
0.3%
2023-01-25T13:27:45.700171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

O3 AQI
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct67
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.424948
Minimum1
Maximum106
Zeros0
Zeros (%)0.0%
Memory size67.9 KiB
2023-01-25T13:27:45.948953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q124
median31
Q338
95-th percentile48
Maximum106
Range105
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.903411
Coefficient of variation (CV)0.37878855
Kurtosis4.494917
Mean31.424948
Median Absolute Deviation (MAD)7
Skewness1.1280572
Sum272580
Variance141.69118
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.711969553 × 10-6
2023-01-25T13:27:46.091923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 544
 
6.3%
25 484
 
5.6%
36 472
 
5.4%
42 350
 
4.0%
33 350
 
4.0%
30 320
 
3.7%
26 304
 
3.5%
28 284
 
3.3%
27 280
 
3.2%
29 274
 
3.2%
Other values (57) 5012
57.8%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 4
 
< 0.1%
3 20
0.2%
4 4
 
< 0.1%
5 12
 
0.1%
6 28
0.3%
7 12
 
0.1%
8 40
0.5%
9 40
0.5%
10 40
0.5%
ValueCountFrequency (%)
106 8
 
0.1%
104 4
 
< 0.1%
100 4
 
< 0.1%
93 4
 
< 0.1%
90 4
 
< 0.1%
87 16
0.2%
84 6
 
0.1%
80 8
 
0.1%
77 20
0.2%
74 8
 
0.1%
2023-01-25T13:27:46.260276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

SO2 Units
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Parts per billion
8674 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters147458
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion 8674
100.0%

Length

2023-01-25T13:27:46.504665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T13:27:46.620954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
parts 8674
33.3%
per 8674
33.3%
billion 8674
33.3%

Most occurring characters

ValueCountFrequency (%)
r 17348
11.8%
17348
11.8%
i 17348
11.8%
l 17348
11.8%
P 8674
 
5.9%
a 8674
 
5.9%
t 8674
 
5.9%
s 8674
 
5.9%
p 8674
 
5.9%
e 8674
 
5.9%
Other values (3) 26022
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 121436
82.4%
Space Separator 17348
 
11.8%
Uppercase Letter 8674
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 17348
14.3%
i 17348
14.3%
l 17348
14.3%
a 8674
7.1%
t 8674
7.1%
s 8674
7.1%
p 8674
7.1%
e 8674
7.1%
b 8674
7.1%
o 8674
7.1%
Space Separator
ValueCountFrequency (%)
17348
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 130110
88.2%
Common 17348
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 17348
13.3%
i 17348
13.3%
l 17348
13.3%
P 8674
6.7%
a 8674
6.7%
t 8674
6.7%
s 8674
6.7%
p 8674
6.7%
e 8674
6.7%
b 8674
6.7%
Other values (2) 17348
13.3%
Common
ValueCountFrequency (%)
17348
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 17348
11.8%
17348
11.8%
i 17348
11.8%
l 17348
11.8%
P 8674
 
5.9%
a 8674
 
5.9%
t 8674
 
5.9%
s 8674
 
5.9%
p 8674
 
5.9%
e 8674
 
5.9%
Other values (3) 26022
17.6%

SO2 Mean
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct876
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5732559
Minimum0
Maximum19.375
Zeros218
Zeros (%)2.5%
Memory size67.9 KiB
2023-01-25T13:27:46.724566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1125
Q10.775
median1.2375
Q32.125
95-th percentile3.9143114
Maximum19.375
Range19.375
Interquartile range (IQR)1.35

Descriptive statistics

Standard deviation1.3593036
Coefficient of variation (CV)0.8640067
Kurtosis36.144113
Mean1.5732559
Median Absolute Deviation (MAD)0.6125
Skewness4.0337795
Sum13646.421
Variance1.8477063
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.169279767 × 10-14
2023-01-25T13:27:47.036305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 388
 
4.5%
0 218
 
2.5%
1.083333 105
 
1.2%
2 98
 
1.1%
1.125 88
 
1.0%
1.5 86
 
1.0%
1.041667 84
 
1.0%
1.166667 64
 
0.7%
0.958333 64
 
0.7%
1.075 63
 
0.7%
Other values (866) 7416
85.5%
ValueCountFrequency (%)
0 218
2.5%
0.0375 36
 
0.4%
0.041667 36
 
0.4%
0.042857 10
 
0.1%
0.043478 4
 
< 0.1%
0.045455 4
 
< 0.1%
0.047619 2
 
< 0.1%
0.06 2
 
< 0.1%
0.066667 2
 
< 0.1%
0.071429 2
 
< 0.1%
ValueCountFrequency (%)
19.375 2
< 0.1%
18.95 2
< 0.1%
17.041667 2
< 0.1%
17.025 2
< 0.1%
16.291667 2
< 0.1%
16.2625 2
< 0.1%
15.708333 2
< 0.1%
15.675 2
< 0.1%
13.333333 2
< 0.1%
13.3125 2
< 0.1%
2023-01-25T13:27:47.218038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Value
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9076551
Minimum0
Maximum30
Zeros218
Zeros (%)2.5%
Memory size67.9 KiB
2023-01-25T13:27:47.469152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.3
median2.3
Q34
95-th percentile7
Maximum30
Range30
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation2.1751437
Coefficient of variation (CV)0.74807487
Kurtosis16.137147
Mean2.9076551
Median Absolute Deviation (MAD)1
Skewness2.6785043
Sum25221
Variance4.7312501
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.216947079 × 10-11
2023-01-25T13:27:47.612784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
2 1680
19.4%
1 1399
16.1%
3 1174
13.5%
4 724
8.3%
5 462
 
5.3%
1.3 373
 
4.3%
1.6 351
 
4.0%
2.3 304
 
3.5%
2.6 258
 
3.0%
6 238
 
2.7%
Other values (37) 1711
19.7%
ValueCountFrequency (%)
0 218
 
2.5%
0.3 78
 
0.9%
0.6 117
 
1.3%
1 1399
16.1%
1.3 373
 
4.3%
1.6 351
 
4.0%
2 1680
19.4%
2.3 304
 
3.5%
2.6 258
 
3.0%
3 1174
13.5%
ValueCountFrequency (%)
30 2
< 0.1%
22 4
< 0.1%
21 2
< 0.1%
20.3 4
< 0.1%
20 2
< 0.1%
19.6 4
< 0.1%
18 2
< 0.1%
17 2
< 0.1%
16 4
< 0.1%
15 2
< 0.1%
2023-01-25T13:27:47.785625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Hour
Numeric time series

NON STATIONARY  SEASONAL  ZEROS 

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.523173
Minimum0
Maximum23
Zeros941
Zeros (%)10.8%
Memory size67.9 KiB
2023-01-25T13:27:48.035236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median11
Q320
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.7159733
Coefficient of variation (CV)0.66960493
Kurtosis-1.4092798
Mean11.523173
Median Absolute Deviation (MAD)8
Skewness-0.015391085
Sum99952
Variance59.536244
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.352598167 × 10-21
2023-01-25T13:27:48.175859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
20 1457
16.8%
8 1143
13.2%
0 941
10.8%
2 885
10.2%
23 635
7.3%
7 610
7.0%
11 512
 
5.9%
19 434
 
5.0%
14 305
 
3.5%
18 276
 
3.2%
Other values (14) 1476
17.0%
ValueCountFrequency (%)
0 941
10.8%
1 58
 
0.7%
2 885
10.2%
3 28
 
0.3%
4 34
 
0.4%
5 102
 
1.2%
6 222
 
2.6%
7 610
7.0%
8 1143
13.2%
9 150
 
1.7%
ValueCountFrequency (%)
23 635
7.3%
22 91
 
1.0%
21 185
 
2.1%
20 1457
16.8%
19 434
 
5.0%
18 276
 
3.2%
17 258
 
3.0%
16 33
 
0.4%
15 32
 
0.4%
14 305
 
3.5%
2023-01-25T13:27:48.335176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

SO2 AQI
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL  ZEROS 

Distinct23
Distinct (%)0.5%
Missing4336
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean4.5382665
Minimum0
Maximum43
Zeros108
Zeros (%)1.2%
Memory size67.9 KiB
2023-01-25T13:27:48.583630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile10
Maximum43
Range43
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.4451514
Coefficient of variation (CV)0.75913378
Kurtosis14.764287
Mean4.5382665
Median Absolute Deviation (MAD)2
Skewness2.5110877
Sum19687
Variance11.869068
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.745867097 × 10-8
2023-01-25T13:27:48.702274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3 1133
 
13.1%
4 859
 
9.9%
1 756
 
8.7%
6 576
 
6.6%
7 376
 
4.3%
9 198
 
2.3%
10 124
 
1.4%
0 108
 
1.2%
11 80
 
0.9%
13 52
 
0.6%
Other values (13) 76
 
0.9%
(Missing) 4336
50.0%
ValueCountFrequency (%)
0 108
 
1.2%
1 756
8.7%
3 1133
13.1%
4 859
9.9%
6 576
6.6%
7 376
 
4.3%
9 198
 
2.3%
10 124
 
1.4%
11 80
 
0.9%
13 52
 
0.6%
ValueCountFrequency (%)
43 2
 
< 0.1%
31 4
< 0.1%
30 2
 
< 0.1%
29 2
 
< 0.1%
26 2
 
< 0.1%
24 2
 
< 0.1%
23 2
 
< 0.1%
21 2
 
< 0.1%
20 2
 
< 0.1%
19 6
0.1%
2023-01-25T13:27:48.845738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

CO Units
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Parts per million
8674 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters147458
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million 8674
100.0%

Length

2023-01-25T13:27:49.093687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T13:27:49.211899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
parts 8674
33.3%
per 8674
33.3%
million 8674
33.3%

Most occurring characters

ValueCountFrequency (%)
r 17348
11.8%
17348
11.8%
i 17348
11.8%
l 17348
11.8%
P 8674
 
5.9%
a 8674
 
5.9%
t 8674
 
5.9%
s 8674
 
5.9%
p 8674
 
5.9%
e 8674
 
5.9%
Other values (3) 26022
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 121436
82.4%
Space Separator 17348
 
11.8%
Uppercase Letter 8674
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 17348
14.3%
i 17348
14.3%
l 17348
14.3%
a 8674
7.1%
t 8674
7.1%
s 8674
7.1%
p 8674
7.1%
e 8674
7.1%
m 8674
7.1%
o 8674
7.1%
Space Separator
ValueCountFrequency (%)
17348
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 130110
88.2%
Common 17348
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 17348
13.3%
i 17348
13.3%
l 17348
13.3%
P 8674
6.7%
a 8674
6.7%
t 8674
6.7%
s 8674
6.7%
p 8674
6.7%
e 8674
6.7%
m 8674
6.7%
Other values (2) 17348
13.3%
Common
ValueCountFrequency (%)
17348
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 17348
11.8%
17348
11.8%
i 17348
11.8%
l 17348
11.8%
P 8674
 
5.9%
a 8674
 
5.9%
t 8674
 
5.9%
s 8674
 
5.9%
p 8674
 
5.9%
e 8674
 
5.9%
Other values (3) 26022
17.6%

CO Mean
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct815
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58174737
Minimum0
Maximum2.15
Zeros20
Zeros (%)0.2%
Memory size67.9 KiB
2023-01-25T13:27:49.311391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.145833
Q10.325
median0.504167
Q30.775
95-th percentile1.266667
Maximum2.15
Range2.15
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.34799088
Coefficient of variation (CV)0.59818212
Kurtosis0.97368299
Mean0.58174737
Median Absolute Deviation (MAD)0.2125
Skewness1.007607
Sum5046.0767
Variance0.12109765
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.940737445 × 10-7
2023-01-25T13:27:49.453810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.433333 66
 
0.8%
0.3 66
 
0.8%
0.333333 62
 
0.7%
0.491667 60
 
0.7%
0.475 58
 
0.7%
0.6 54
 
0.6%
0.341667 54
 
0.6%
0.304167 54
 
0.6%
0.354167 54
 
0.6%
0.2625 52
 
0.6%
Other values (805) 8094
93.3%
ValueCountFrequency (%)
0 20
0.2%
0.004167 4
 
< 0.1%
0.00625 2
 
< 0.1%
0.008333 4
 
< 0.1%
0.008696 2
 
< 0.1%
0.009091 2
 
< 0.1%
0.009524 2
 
< 0.1%
0.01 2
 
< 0.1%
0.0125 4
 
< 0.1%
0.016667 6
 
0.1%
ValueCountFrequency (%)
2.15 2
< 0.1%
2.120833 2
< 0.1%
2.079167 2
< 0.1%
2.033333 2
< 0.1%
2.008696 2
< 0.1%
1.970833 2
< 0.1%
1.933333 2
< 0.1%
1.908333 2
< 0.1%
1.904167 2
< 0.1%
1.895833 2
< 0.1%
2023-01-25T13:27:49.623424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Value
Real number (ℝ)

Distinct50
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1952963
Minimum0
Maximum5.5
Zeros20
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2023-01-25T13:27:49.891123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.7
median1
Q31.6
95-th percentile2.6
Maximum5.5
Range5.5
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.72379849
Coefficient of variation (CV)0.60553897
Kurtosis2.0691479
Mean1.1952963
Median Absolute Deviation (MAD)0.4
Skewness1.2056109
Sum10368
Variance0.52388425
MonotonicityNot monotonic
2023-01-25T13:27:50.029508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 626
 
7.2%
0.7 594
 
6.8%
0.9 592
 
6.8%
0.6 582
 
6.7%
1 534
 
6.2%
0.5 516
 
5.9%
1.1 448
 
5.2%
0.4 438
 
5.0%
1.2 436
 
5.0%
1.3 434
 
5.0%
Other values (40) 3474
40.1%
ValueCountFrequency (%)
0 20
 
0.2%
0.1 68
 
0.8%
0.2 168
 
1.9%
0.3 288
3.3%
0.4 438
5.0%
0.5 516
5.9%
0.6 582
6.7%
0.7 594
6.8%
0.8 626
7.2%
0.9 592
6.8%
ValueCountFrequency (%)
5.5 4
< 0.1%
5 2
 
< 0.1%
4.9 4
< 0.1%
4.7 2
 
< 0.1%
4.6 2
 
< 0.1%
4.5 2
 
< 0.1%
4.4 2
 
< 0.1%
4.3 6
0.1%
4.2 2
 
< 0.1%
4.1 2
 
< 0.1%

CO 1st Max Hour
Real number (ℝ)

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.356352
Minimum0
Maximum23
Zeros2160
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2023-01-25T13:27:50.157038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation9.5473449
Coefficient of variation (CV)0.84070524
Kurtosis-1.7788509
Mean11.356352
Median Absolute Deviation (MAD)8
Skewness-0.022125175
Sum98505
Variance91.151795
MonotonicityNot monotonic
2023-01-25T13:27:50.271626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 2160
24.9%
23 1236
14.2%
7 734
 
8.5%
20 684
 
7.9%
21 680
 
7.8%
19 654
 
7.5%
1 631
 
7.3%
22 554
 
6.4%
8 355
 
4.1%
2 216
 
2.5%
Other values (14) 770
 
8.9%
ValueCountFrequency (%)
0 2160
24.9%
1 631
 
7.3%
2 216
 
2.5%
3 74
 
0.9%
4 24
 
0.3%
5 34
 
0.4%
6 166
 
1.9%
7 734
 
8.5%
8 355
 
4.1%
9 72
 
0.8%
ValueCountFrequency (%)
23 1236
14.2%
22 554
6.4%
21 680
7.8%
20 684
7.9%
19 654
7.5%
18 212
 
2.4%
17 56
 
0.6%
16 10
 
0.1%
15 6
 
0.1%
14 6
 
0.1%

CO AQI
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct34
Distinct (%)0.8%
Missing4323
Missing (%)49.8%
Infinite0
Infinite (%)0.0%
Mean11.449552
Minimum0
Maximum38
Zeros14
Zeros (%)0.2%
Memory size67.9 KiB
2023-01-25T13:27:50.390659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median10
Q315
95-th percentile24
Maximum38
Range38
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.5240964
Coefficient of variation (CV)0.56981238
Kurtosis0.87074124
Mean11.449552
Median Absolute Deviation (MAD)4
Skewness0.95917742
Sum49817
Variance42.563834
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.760995906 × 10-5
2023-01-25T13:27:50.550127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
7 386
 
4.5%
8 344
 
4.0%
9 342
 
3.9%
10 334
 
3.9%
6 328
 
3.8%
5 296
 
3.4%
11 292
 
3.4%
15 212
 
2.4%
13 210
 
2.4%
14 188
 
2.2%
Other values (24) 1419
 
16.4%
(Missing) 4323
49.8%
ValueCountFrequency (%)
0 14
 
0.2%
1 44
 
0.5%
2 118
 
1.4%
3 186
2.1%
5 296
3.4%
6 328
3.8%
7 386
4.5%
8 344
4.0%
9 342
3.9%
10 334
3.9%
ValueCountFrequency (%)
38 2
 
< 0.1%
36 2
 
< 0.1%
35 10
 
0.1%
34 10
 
0.1%
33 24
0.3%
32 8
 
0.1%
31 4
 
< 0.1%
30 16
0.2%
28 10
 
0.1%
27 28
0.3%
2023-01-25T13:27:50.718842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Interactions

2023-01-25T13:27:36.557864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:06.972630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:09.111349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:11.031990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:12.895957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:14.989749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:16.869136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:18.953714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:20.975566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:22.880873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:24.937171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-01-25T13:27:10.673322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:12.549057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:14.473856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:16.515320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:18.553857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:20.479038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:22.522987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:24.552047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:26.529017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:28.574711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:30.429953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:32.292739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:34.339134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:36.212519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:38.290177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:08.859973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:10.790301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:12.663926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:14.592536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:16.631007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:18.684698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:20.595196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:22.641733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:24.679368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:26.649561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:28.693613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:30.544883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:32.406419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:34.453429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:36.326795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:38.408671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:08.978496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:10.903362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:12.773557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:14.862111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:16.742332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:18.811791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:20.703607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:22.754647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:24.800510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:26.765184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:28.804022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:30.656518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:32.517455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:34.567085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T13:27:36.435840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-01-25T13:27:51.150868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
NO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO MeanCO 1st Max ValueCO 1st Max HourCO AQI
NO2 Mean1.0000.8090.0300.809-0.592-0.303-0.197-0.3030.4680.5430.3100.5490.7700.7330.1460.746
NO2 1st Max Value0.8091.0000.1691.000-0.2680.043-0.1640.0430.4010.4840.3760.4910.5790.6190.2490.594
NO2 1st Max Hour0.0300.1691.0000.1700.0870.2320.1300.2330.002-0.0380.122-0.040-0.057-0.0740.269-0.130
NO2 AQI0.8091.0000.1701.000-0.2690.043-0.1640.0430.4010.4850.3770.4920.5790.6190.2500.594
O3 Mean-0.592-0.2680.087-0.2691.0000.8640.2770.863-0.245-0.329-0.245-0.331-0.590-0.543-0.109-0.561
O3 1st Max Value-0.3030.0430.2320.0430.8641.0000.2771.000-0.102-0.175-0.091-0.177-0.420-0.3680.003-0.403
O3 1st Max Hour-0.197-0.1640.130-0.1640.2770.2771.0000.276-0.056-0.103-0.158-0.102-0.174-0.217-0.116-0.231
O3 AQI-0.3030.0430.2330.0430.8631.0000.2761.000-0.102-0.174-0.091-0.177-0.420-0.3670.003-0.402
SO2 Mean0.4680.4010.0020.401-0.245-0.102-0.056-0.1021.0000.8630.1690.8430.4130.3990.0440.415
SO2 1st Max Value0.5430.484-0.0380.485-0.329-0.175-0.103-0.1740.8631.0000.3251.0000.5140.5330.0880.540
SO2 1st Max Hour0.3100.3760.1220.377-0.245-0.091-0.158-0.0910.1690.3251.0000.4310.2350.2910.3150.245
SO2 AQI0.5490.491-0.0400.492-0.331-0.177-0.102-0.1770.8431.0000.4311.0000.5210.5420.0850.546
CO Mean0.7700.579-0.0570.579-0.590-0.420-0.174-0.4200.4130.5140.2350.5211.0000.8810.0360.930
CO 1st Max Value0.7330.619-0.0740.619-0.543-0.368-0.217-0.3670.3990.5330.2910.5420.8811.0000.1361.000
CO 1st Max Hour0.1460.2490.2690.250-0.1090.003-0.1160.0030.0440.0880.3150.0850.0360.1361.0000.095
CO AQI0.7460.594-0.1300.594-0.561-0.403-0.231-0.4020.4150.5400.2450.5460.9301.0000.0951.000

Missing values

2023-01-25T13:27:38.655201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-25T13:27:39.285843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-25T13:27:39.537665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
041330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-01Parts per billion47.208333102.021101Parts per million0.0197500.039933Parts per billion1.7500002.003.0Parts per million0.7875001.922NaN
141330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-01Parts per billion47.208333102.021101Parts per million0.0197500.039933Parts per billion1.7500002.003.0Parts per million0.6210531.32315.0
241330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-01Parts per billion47.208333102.021101Parts per million0.0197500.039933Parts per billion1.7375002.02NaNParts per million0.7875001.922NaN
341330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-01Parts per billion47.208333102.021101Parts per million0.0197500.039933Parts per billion1.7375002.02NaNParts per million0.6210531.32315.0
441330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-02Parts per billion28.08333379.02378Parts per million0.0142500.0271023Parts per billion1.3750002.003.0Parts per million0.4750001.522NaN
541330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-02Parts per billion28.08333379.02378Parts per million0.0142500.0271023Parts per billion1.3750002.003.0Parts per million0.5541671.3015.0
641330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-02Parts per billion28.08333379.02378Parts per million0.0142500.0271023Parts per billion1.3625002.02NaNParts per million0.4750001.522NaN
741330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-02Parts per billion28.08333379.02378Parts per million0.0142500.0271023Parts per billion1.3625002.02NaNParts per million0.5541671.3015.0
841330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-03Parts per billion62.714286170.019114Parts per million0.0162920.0322227Parts per billion1.0333333.320NaNParts per million1.1791671.71119.0
941330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-03Parts per billion62.714286170.019114Parts per million0.0162920.0322227Parts per billion1.0333333.320NaNParts per million1.2227273.67NaN
State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
866441330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-29Parts per billion16.04545528.01326Parts per million0.0179170.0352230Parts per billion1.3478263.094.0Parts per million0.2478260.40NaN
866541330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-29Parts per billion16.04545528.01326Parts per million0.0179170.0352230Parts per billion1.3478263.094.0Parts per million0.3541670.809.0
866641330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-30Parts per billion11.29166735.02233Parts per million0.0245830.037831Parts per billion1.0750001.623NaNParts per million0.2416670.822NaN
866741330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-30Parts per billion11.29166735.02233Parts per million0.0245830.037831Parts per billion1.0750001.623NaNParts per million0.1750000.5236.0
866841330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-30Parts per billion11.29166735.02233Parts per million0.0245830.037831Parts per billion1.0833332.0223.0Parts per million0.2416670.822NaN
866941330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-30Parts per billion11.29166735.02233Parts per million0.0245830.037831Parts per billion1.0833332.0223.0Parts per million0.1750000.5236.0
867041330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-31Parts per billion18.45833326.0025Parts per million0.0180530.0311426Parts per billion1.0833332.073.0Parts per million0.4666670.728.0
867141330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-31Parts per billion18.45833326.0025Parts per million0.0180530.0311426Parts per billion1.0750001.38NaNParts per million0.4041670.87NaN
867241330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-31Parts per billion18.45833326.0025Parts per million0.0180530.0311426Parts per billion1.0833332.073.0Parts per million0.4041670.87NaN
867341330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-31Parts per billion18.45833326.0025Parts per million0.0180530.0311426Parts per billion1.0750001.38NaNParts per million0.4666670.728.0